Anomaly Detection and Qualitative Analysis of Diseases in Tomato

Disease detection in the crops is a difficult work as crops get affected due to various attacks from different bacteria, fungi and viruses. The disease symptoms on the infected crop plant can be seen as usually color change, circular spots, specks and hollow areas having concentric rings. This paper proposes a solution for identification of crop diseases (i.e., bacterial and fungal diseases) in tomato cash crop of Himachal Pradesh. Detecting the disease at an early stage enables the farmers to act and treat the plants at the appropriate time and effectively. Accurate and timely detection of plant diseases can help mitigate the agriculture loss experienced by the local farmers. An initial evaluation system and statistical analysis proposed in this work show a positive impact. The dataset has been created by the authors by collecting real-time pictures from various fields of Himachal Pradesh state which contains images with different diseases for tomato plant. The proposed approach provides efficient result that can lead to connection between farmers and agriculturists.